Modern cellular networks face the challenge that the demand for data traffic is increasing dramatically. Network operators need to modify their networks to increase the overall capacity. In both homogenous and heterogeneous networks, the mobile user equipment (UE) is faced not only with varying channel conditions, but also with multiple interfering cells whose signals and channels likewise exhibit time/frequency-selective behavior. In addition, modem frontend non-idealities such as timing/frequency offsets and signal level variations, are often required to be compensated for by channel estimation. Channel estimation filtering, which is key to the proper receiver operation, therefore faces multiple challenges which can be grouped into the following five categories:
(1.) reference signal configuration (R): the reference signal (RS) pattern in time/frequency plane, relative to resource element (RE) positions for which the channel is to be estimated may dynamically change, depending on the standard, transmission mode, logical channel (in LTE, e.g., CRS for PDCCH and TM1-6 PDSCH, DMRS for TM7-10 PDSCH), time (first/last slots, TDD special SF, MBSFN SF, etc.), and frequency (edges of OFDM spectrum). (2.) channel parameters (C): the physical channel parameters may change with time, in particular, delay spread/shift and Doppler spread/shift. (3.) noise parameters (N): the level of thermal noise and background interference (not considered in interference mitigation) and thus the SNR may also change with time. (4.) interference parameters (I): depending on interferer scheduling, the interference/SIR levels may be strongly dynamic across the 2D time/frequency plane of the post-FFT OFDM signal. In LTE-A FeICIC (further enhanced inter-cell interference coordination) scenarios, the interference may be scheduled differently for each physical resource block (PRB), hence the SIR pattern granularity may be as small as one PRB. (5.) synchronization errors (S): some of the frontend/AGC non-idealities such as carrier frequency offset, timing offset, and signal gain variations may also affect channel estimation.
It may thus be desirable to provide a new technique for improving channel estimation for fast adaptation to dynamic changes of conditions as described above, in particular, interference patterns (I) and, to some extent, synchronization errors (S).